1. Gastos (cálculos antiguos)

Gastos_casa %>% 
  dplyr::select(-Tiempo,-link) %>%
  dplyr::select(fecha, gasto, monto, gastador,obs) %>% tail(30) %>% 
  knitr::kable(format = "markdown", size=12)
fecha gasto monto gastador obs
4/3/2024 Comida 6000 Andrés pasas
5/3/2024 Uber cumple papá 8582 Tami NA
6/3/2024 Agua 16549 Andrés NA
7/3/2024 Enceres 4645 Andrés descuentos desodorantes
10/3/2024 Comida 7470 Andrés NA
10/3/2024 Comida 90504 Tami Supermercado
10/3/2024 Diosi 21081 Andrés pipeta
11/3/2024 Diosi 66970 Andrés n&d 50990 + lavanda 3asy clean 10k 79990x2
17/3/2024 Comida 55951 Tami Supermercado
19/3/2024 VTR 21990 Andrés NA
24/3/2024 Comida 94384 Tami Supermercado
27/3/2024 Comida 27980 Tami Barras Wild Soul
27/3/2024 Electricidad 56338 Andrés NA
29/3/2024 Comida 69144 Tami Supermercado
1/4/2024 Comida 11990 Andrés vino
1/4/2024 Comida 21300 Andrés piwen
2/4/2024 Comida 34980 Tami bar providencia
3/4/2024 Comida 30969 Tami Supermercado
5/4/2024 Enceres 24990 Tami Shampoo
5/4/2024 Enceres 16600 Tami Acondicionador cabello
6/4/2024 Enceres 16600 Andrés Acondicionador cabello
7/4/2024 Comida 33828 Tami Supermercado
10/4/2024 Comida 20230 Tami Supermercado
12/4/2024 Comida 7000 Andrés NA
13/4/2024 Comida 22800 Andrés empanadas
14/4/2024 Comida 75094 Tami Supermercado
18/4/2024 Parafina 49376 Tami NA
19/4/2024 VTR 21990 Andrés NA
31/3/2019 Comida 9000 Andrés NA
8/9/2019 Comida 24588 Andrés Super Lider

#para ver las diferencias depués de la diosi
Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
    dplyr::group_by(gastador, fecha,.drop = F) %>% 
    dplyr::summarise(gasto_media=mean(monto,na.rm=T)) %>% 
    dplyr::mutate(treat=ifelse(fecha>"2019-W26",1,0)) %>%
    #dplyr::mutate(fecha_simp=lubridate::week(fecha)) %>%#después de  diosi. Junio 24, 2019 
    dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>% 
    assign("ts_gastos_casa_week_treat", ., envir = .GlobalEnv) 

gplots::plotmeans(gasto_media ~ gastador_nombre, main="Promedio de gasto por gastador", data=ts_gastos_casa_week_treat,ylim=c(0,75000), xlab="", ylab="")

par(mfrow=c(1,2)) 
gplots::plotmeans(gasto_media ~ gastador_nombre, main="Antes de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==0,], xlab="", ylab="", ylim=c(0,70000))

gplots::plotmeans(gasto_media ~ gastador_nombre, main="Después de Diosi", data=ts_gastos_casa_week_treat[ts_gastos_casa_week_treat$treat==1,], xlab="", ylab="",ylim=c(0,70000))

library(ggiraph)
library(scales)
#if( requireNamespace("dplyr", quietly = TRUE)){
gg <- Gastos_casa %>%
  dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
  dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
  dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%
  dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
  dplyr::mutate(treat=ifelse(fecha_week>"2019 W26",1,0)) %>%
  dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>% 
#  dplyr::mutate(week=as.Date(as.character(lubridate::floor_date(fecha, "week"))))%>%
  #dplyr::mutate(fecha_week= lubridate::parse_date_time(fecha_week, c("%Y-W%V"),exact=T)) %>% 
  dplyr::group_by(gastador_nombre, fecha_simp) %>%
  dplyr::summarise(monto_total=sum(monto)) %>%
  dplyr::mutate(tooltip= paste0(substr(gastador_nombre,1,1),"=",round(monto_total/1000,2))) %>%
  ggplot(aes(hover_css = "fill:none;")) +#, ) +
  #stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
  geom_line(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre)),size=1,alpha=.5) +
                       ggiraph::geom_point_interactive(aes(x = fecha_simp, y = monto_total, color=as.factor(gastador_nombre),tooltip=tooltip),size = 1) +
  #geom_text(aes(x = fech_ing_qrt, y = perc_dup-0.05, label = paste0(n)), vjust = -1,hjust = 0, angle=45, size=3) +
 # guides(color = F)+
  theme_custom() +
  geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
  labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") + ggtitle( "Figura 4. Gastos por Gastador") +
  scale_y_continuous(labels = f <- function(x) paste0(x/1000)) + 
  scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
  scale_x_yearweek(date_breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
  theme(axis.text.x = element_text(vjust = 0.5,angle = 35), legend.position='bottom')+
     theme(
    panel.border = element_blank(), 
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(), 
    axis.line = element_line(colour = "black")
    )

#  x <- girafe(ggobj = gg)
#  x <- girafe_options(x = x,
#                      opts_hover(css = "stroke:red;fill:orange") )
#  if( interactive() ) print(x)

#}
tooltip_css <- "background-color:gray;color:white;font-style:italic;padding:10px;border-radius:10px 20px 10px 20px;"

#ggiraph(code = {print(gg)}, tooltip_extra_css = tooltip_css, tooltip_opacity = .75 )

x <- girafe(ggobj = gg)
x <- girafe_options(x,
  opts_zoom(min = 1, max = 3), opts_hover(css =tooltip_css))
x
plot<-Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(month=as.Date(as.character(lubridate::floor_date(fecha, "month"))))%>%
    dplyr::group_by(month)%>%
    dplyr::summarise(gasto_total=sum(monto)/1000) %>%
      ggplot2::ggplot(aes(x = month, y = gasto_total)) +
      geom_point()+
      geom_line(size=1) +
      theme_custom() +
      geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
      geom_vline(xintercept = as.Date("2019-03-23"),linetype = "dashed", color="red") +
      labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") + 
      ggtitle( "Figura. Suma de Gastos por Mes") +        
      scale_x_date(breaks = "1 month", minor_breaks = "1 month", labels=scales::date_format("%m/%y")) +
      theme(axis.text.x = element_text(vjust = 0.5,angle = 45)) 
plotly::ggplotly(plot)  
plot2<-Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
    dplyr::group_by(day)%>%
    summarise(gasto_total=sum(monto)/1000) %>%
      ggplot2::ggplot(aes(x = day, y = gasto_total)) +
      geom_line(size=1) +
      theme_custom() +
      geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
      geom_vline(xintercept = as.Date("2020-03-23"),linetype = "dashed", color="red") +
      labs(y="Gastos (en miles)",x="Meses/Año", subtitle="Interlineado, incorporación de la Diosi") + 
      ggtitle( "Figura. Suma de Gastos por Día") +        
      scale_x_date(breaks = "1 month", minor_breaks = "1 week", labels=scales::date_format("%m/%y")) +
      theme(axis.text.x = element_text(vjust = 0.5,angle = 45)) 
plotly::ggplotly(plot2)  
tsData <- Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
    dplyr::group_by(day)%>%
    summarise(gasto_total=sum(monto))%>%
    dplyr::mutate(covid=case_when(day>as.Date("2019-06-02")~1,TRUE~0))%>%
    dplyr::mutate(covid=case_when(day>as.Date("2020-03-10")~covid+1,TRUE~covid))%>%
    dplyr::mutate(covid=as.factor(covid))%>%
  data.frame()
tsData_gastos <-ts(tsData$gasto_total, frequency=7)
mstsData_gastos <- forecast::msts(Gastos_casa$monto, seasonal.periods=c(7,30))
  tsData_gastos = decompose(tsData_gastos)
#plot(tsData_Santiago, title="Descomposición del número de casos confirmados para Santiago")
forecast::autoplot(tsData_gastos, main="Descomposición de los Gastos Diarios")+
    theme_bw()+ labs(x="Weeks")

tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()
#tsData_gastos$trend
#Using the inputted variables, a Type-2 Sum Squares ANCOVA Lagged Dependent Variable model is fitted which estimates the difference in means between interrupted and non-interrupted time periods, while accounting for the lag of the dependent variable and any further specified covariates.
#Typically such analyses use Auto-regressive Integrated Moving Average (ARIMA) models to handle the serial dependence of the residuals of a linear model, which is estimated either as part of the ARIMA process or through a standard linear regression modeling process [9,17]. All such time series methods enable the effect of the event to be separated from general trends and serial dependencies in time, thereby enabling valid statistical inferences to be made about whether an intervention has had an effect on a time series.
   #it uses Type-2 Sum Squares ANCOVA Lagged Dependent Variable model
   #ITSA model da cuenta de observaciones autocorrelacionadas e impactos dinámicos mediante una regresión de deltas en rezagados. Una vez que se incorporan en el modelo, se controlan. 
#residual autocorrelation assumptions
#TSA allows the model to account for baseline levels and trends present in the data therefore allowing us to attribute significant changes to the interruption
#RDestimate(all~agecell,data=metro_region,cutpoint = 21)
tsdata_gastos_trend<-cbind(tsData,trend=as.vector(tsData_gastos$trend))%>% na.omit()

itsa_metro_region_quar2<-
        its.analysis::itsa.model(time = "day", depvar = "trend",data=tsdata_gastos_trend,
                                 interrupt_var = "covid", 
                                 alpha = 0.05,no.plots = F, bootstrap = TRUE, Reps = 10000, print = F) 

print(itsa_metro_region_quar2)
## [[1]]
## [1] "ITSA Model Fit"
## 
## $aov.result
## Anova Table (Type II tests)
## 
## Response: depvar
##                   Sum Sq  Df   F value Pr(>F)    
## interrupt_var 7.9536e+08   2    7.6048  5e-04 ***
## lag_depvar    1.0342e+11   1 1977.6963 <2e-16 ***
## Residuals     3.6448e+10 697                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## $tukey.result
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: stats::aov(formula = x$depvar ~ x$interrupt_var)
## 
## $`x$interrupt_var`
##          diff        lwr      upr     p adj
## 1-0  7228.838   910.7709 13546.91 0.0201442
## 2-0 29062.362 23340.9616 34783.76 0.0000000
## 2-1 21833.524 18480.8933 25186.16 0.0000000
## 
## 
## $data
##        depvar interrupt_var lag_depvar
## 2    19269.29             0   16010.00
## 3    24139.00             0   19269.29
## 4    23816.14             0   24139.00
## 5    26510.14             0   23816.14
## 6    23456.71             0   26510.14
## 7    24276.71             0   23456.71
## 8    18818.71             0   24276.71
## 9    18517.14             0   18818.71
## 10   15475.29             0   18517.14
## 11   16365.29             0   15475.29
## 12   12621.29             0   16365.29
## 13   12679.86             0   12621.29
## 14   13440.71             0   12679.86
## 15   15382.86             0   13440.71
## 16   13459.71             0   15382.86
## 17   14644.14             0   13459.71
## 18   13927.00             0   14644.14
## 19   22034.57             0   13927.00
## 20   20986.00             0   22034.57
## 21   20390.57             0   20986.00
## 22   22554.14             0   20390.57
## 23   21782.57             0   22554.14
## 24   22529.57             0   21782.57
## 25   24642.71             0   22529.57
## 26   17692.29             0   24642.71
## 27   19668.29             0   17692.29
## 28   28640.00             0   19668.29
## 29   28706.00             0   28640.00
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## 441  37966.43             2   38303.00
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## 553  50565.00             2   49242.43
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## 556  58787.86             2   49786.71
## 557  58060.86             2   58787.86
## 558  62179.43             2   58060.86
## 559  57333.86             2   62179.43
## 560  70797.00             2   57333.86
## 561  89901.71             2   70797.00
## 562  78558.14             2   89901.71
## 563  65466.00             2   78558.14
## 564  70525.00             2   65466.00
## 565  68377.86             2   70525.00
## 566  69736.29             2   68377.86
## 567  60085.86             2   69736.29
## 568  41757.00             2   60085.86
## 569  49780.29             2   41757.00
## 570  56540.29             2   49780.29
## 571  57894.29             2   56540.29
## 572  60270.29             2   57894.29
## 573  61011.00             2   60270.29
## 574  57721.43             2   61011.00
## 575  71741.00             2   57721.43
## 576  59576.00             2   71741.00
## 577  52390.29             2   59576.00
## 578  61092.29             2   52390.29
## 579  62814.00             2   61092.29
## 580  54908.29             2   62814.00
## 581  62082.00             2   54908.29
## 582  57017.71             2   62082.00
## 583  53634.43             2   57017.71
## 584  69169.00             2   53634.43
## 585  52488.14             2   69169.00
## 586  60895.57             2   52488.14
## 587  59856.57             2   60895.57
## 588  52670.00             2   59856.57
## 589  51874.57             2   52670.00
## 590  52190.57             2   51874.57
## 591  41562.43             2   52190.57
## 592  44764.14             2   41562.43
## 593  38612.71             2   44764.14
## 594  43473.14             2   38612.71
## 595  53505.00             2   43473.14
## 596  45870.86             2   53505.00
## 597  52578.00             2   45870.86
## 598  55300.00             2   52578.00
## 599  61789.71             2   55300.00
## 600  57391.71             2   61789.71
## 601  62902.29             2   57391.71
## 602  53250.43             2   62902.29
## 603  55402.57             2   53250.43
## 604  56291.29             2   55402.57
## 605  58933.57             2   56291.29
## 606  59590.71             2   58933.57
## 607  59065.00             2   59590.71
## 608  52399.57             2   59065.00
## 609  60483.43             2   52399.57
## 610  58262.71             2   60483.43
## 611  54939.71             2   58262.71
## 612  51169.00             2   54939.71
## 613  43113.29             2   51169.00
## 614  56289.71             2   43113.29
## 615  60739.86             2   56289.71
## 616  50363.14             2   60739.86
## 617  62270.86             2   50363.14
## 618  67061.57             2   62270.86
## 619  59609.00             2   67061.57
## 620  85054.00             2   59609.00
## 621  68023.29             2   85054.00
## 622  59242.29             2   68023.29
## 623  61535.14             2   59242.29
## 624  56215.86             2   61535.14
## 625  45152.29             2   56215.86
## 626  57409.57             2   45152.29
## 627  35151.43             2   57409.57
## 628  34991.43             2   35151.43
## 629  45944.71             2   34991.43
## 630  57944.71             2   45944.71
## 631  55706.29             2   57944.71
## 632  88593.71             2   55706.29
## 633  77359.43             2   88593.71
## 634  79878.71             2   77359.43
## 635  81753.00             2   79878.71
## 636  75716.00             2   81753.00
## 637  67381.43             2   75716.00
## 638  63528.57             2   67381.43
## 639  49682.86             2   63528.57
## 640  47815.00             2   49682.86
## 641  46546.14             2   47815.00
## 642  44808.71             2   46546.14
## 643  42959.57             2   44808.71
## 644  46023.86             2   42959.57
## 645  51309.57             2   46023.86
## 646  68447.29             2   51309.57
## 647  84959.29             2   68447.29
## 648  81666.29             2   84959.29
## 649  82700.86             2   81666.29
## 650  89422.14             2   82700.86
## 651 104812.71             2   89422.14
## 652  98812.71             2  104812.71
## 653  64779.86             2   98812.71
## 654  61862.86             2   64779.86
## 655  58376.43             2   61862.86
## 656  59503.57             2   58376.43
## 657  55429.43             2   59503.57
## 658  44454.57             2   55429.43
## 659  47184.00             2   44454.57
## 660  52126.71             2   47184.00
## 661  51202.00             2   52126.71
## 662  64437.14             2   51202.00
## 663  64297.14             2   64437.14
## 664  64628.57             2   64297.14
## 665  51413.14             2   64628.57
## 666  52969.43             2   51413.14
## 667  54135.29             2   52969.43
## 668  48799.43             2   54135.29
## 669  41907.86             2   48799.43
## 670  45382.00             2   41907.86
## 671  42633.29             2   45382.00
## 672  46624.71             2   42633.29
## 673  44051.86             2   46624.71
## 674  35852.86             2   44051.86
## 675  29737.71             2   35852.86
## 676  29734.86             2   29737.71
## 677  32881.71             2   29734.86
## 678  38298.57             2   32881.71
## 679  40886.14             2   38298.57
## 680  38601.86             2   40886.14
## 681  38628.86             2   38601.86
## 682  39142.57             2   38628.86
## 683  32666.14             2   39142.57
## 684  39911.57             2   32666.14
## 685  39336.29             2   39911.57
## 686  39678.86             2   39336.29
## 687  41963.14             2   39678.86
## 688  54220.57             2   41963.14
## 689  63901.86             2   54220.57
## 690  73116.00             2   63901.86
## 691  60863.86             2   73116.00
## 692  56293.86             2   60863.86
## 693  52725.00             2   56293.86
## 694  55525.00             2   52725.00
## 695  44413.00             2   55525.00
## 696  37200.14             2   44413.00
## 697  30212.43             2   37200.14
## 698  26456.71             2   30212.43
## 699  24716.71             2   26456.71
## 700  31020.29             2   24716.71
## 701  32132.57             2   31020.29
## 702  32902.57             2   32132.57
## 
## $alpha
## [1] 0.05
## 
## $itsa.result
## [1] "Significant variation between time periods with chosen alpha"
## 
## $group.means
##   interrupt_var count     mean      s.d.
## 1             0    37 22066.04  6308.636
## 2             1   120 29463.10  9187.258
## 3             2   545 51296.62 15365.008
## 
## $dependent
##   [1]  19269.29  24139.00  23816.14  26510.14  23456.71  24276.71  18818.71
##   [8]  18517.14  15475.29  16365.29  12621.29  12679.86  13440.71  15382.86
##  [15]  13459.71  14644.14  13927.00  22034.57  20986.00  20390.57  22554.14
##  [22]  21782.57  22529.57  24642.71  17692.29  19668.29  28640.00  28706.00
##  [29]  28331.57  25617.86  27223.29  31622.57  32021.43  33634.57  30784.86
##  [36]  34770.57  38443.00  35073.00  31422.29  30103.29  19319.29  27926.29
##  [43]  30715.43  31962.29  39790.14  39211.57  44548.57  49398.00  41039.00
##  [50]  34821.29  29123.57  21275.71  28476.14  24561.86  20323.57  25370.00
##  [57]  26811.86  27151.86  27623.29  22896.57  41889.29  44000.14  38558.00
##  [64]  43373.86  49001.00  61213.29  58939.57  42046.86  39191.71  42646.43
##  [71]  36121.57  30915.57  20273.43  23938.29  19274.29  21662.29  15819.00
##  [78]  18126.14  17240.71  16127.71  13917.14  15379.86  19510.14  24567.29
##  [85]  25700.43  25729.00  26435.00  31157.14  29818.43  30962.43  28746.71
##  [92]  27830.71  28252.14  28717.57  21365.43  24816.86  16838.57  15529.14
##  [99]  13286.29  13629.43  14404.86  19524.86  18475.71  22495.00  22254.57
## [106]  24173.29  27466.43  24602.43  20531.14  20846.43  23875.71  36312.71
## [113]  34244.00  36347.43  39779.71  42018.71  39372.57  33444.00  29255.86
## [120]  31640.14  29671.14  31023.71  39723.43  39314.14  38239.86  34649.43
## [127]  36688.43  42867.57  42226.86  32155.14  33603.00  37254.43  33145.57
## [134]  31299.43  30252.00  26310.71  27929.86  27666.14  25017.57  27335.00
## [141]  25760.71  18436.86  21906.00  19418.14  22826.14  23444.29  25264.86
## [148]  25473.29  27366.86  28855.86  32326.86  27141.43  26297.71  23499.14
## [155]  30246.29  39931.86  38020.43  35004.00  40750.86  42363.29  46273.57
## [162]  41083.29  35711.29  41921.71  60583.29  63115.57  61300.14  57666.43
## [169]  55834.00  58927.71  57810.57  48987.14  52219.29  56503.57  56545.00
## [176]  64705.57  53833.29  50114.00  39592.43  29907.29  33923.29  45489.00
## [183]  44866.29  51680.57  58257.00  70600.57  76648.00  69430.14  69651.57
## [190]  77745.14  72795.86  67670.71  55357.86  48524.00  50154.43  45111.57
## [197]  36147.00  43501.57  41472.43  41058.00  41605.57  49382.86  59558.57
## [204]  59134.57  61109.00  63004.43  67344.29  78180.86  69117.86  55597.57
## [211]  49426.14  39119.43  35636.86  39201.14  27777.00  47207.00  55587.29
## [218]  56619.71  82679.86  91259.57  93552.71 102242.71  91884.00  85013.86
## [225]  84535.29  80700.43  79740.57  85163.14  86724.86  80355.00  74875.14
## [232]  81347.00  66062.43  56946.43  47732.14  38129.71  42928.29  45392.57
## [239]  37895.43  30660.29  42430.86  35845.14  40350.43  31494.71  30013.29
## [246]  34197.57  37430.14  26932.43  33729.86  38081.43  44028.00  47139.71
## [253]  46558.86  58350.57  78380.00  78168.29  70510.86  72207.14  67881.00
## [260]  69536.43  62390.71  50113.14  45565.57  45805.29  41348.57  51426.86
## [267]  47160.57  51907.43  49751.43  54407.43  54746.29  61634.57  58926.43
## [274]  69999.29  63044.86  63285.29  61395.43  67969.43  60792.57  56859.14
## [281]  44899.43  43064.14  62790.29  69120.71  69589.43  66633.29  65588.57
## [288]  70168.57  74644.71  52891.00  41560.57  34704.86  46520.00  50231.00
## [295]  49216.71  76914.86  83720.71  84485.00  89765.00  87702.86  82013.86
## [302]  85982.43  57248.43  52968.43  52601.86  45493.29  42298.86  46423.71
## [309]  37898.00  36435.14  30209.57  34541.86  33604.71  37990.71  35683.43
## [316]  65201.86  62730.57  64589.14  73744.86  76477.71 105647.43 103790.29
## [323]  76122.29  74746.14  72865.71  63652.57  60358.29  25957.14  30178.43
## [330]  30681.57  33337.29  32582.71  39184.43  40415.71  34975.43  34076.14
## [337]  34221.14  28862.57  35729.86  36489.29  36785.14  37787.71  39832.14
## [344]  41917.86  41633.57  33557.00  22759.57  28877.86  27574.00  27104.71
## [351]  24376.14  29732.29  34030.00  39139.71  37066.57  38509.29  40957.29
## [358]  49423.00  50053.29  50284.14  53103.86  50223.00  49587.14  41167.71
## [365]  37958.71  33582.29  31039.43  26526.57  34869.43  37487.43  46514.43
## [372]  39613.43  38980.57  37306.14  36771.29  26317.00  31580.71  23626.57
## [379]  33035.71  44864.57  48946.14  46969.57  49249.57  56370.14  67228.71
## [386]  59457.29  53124.71  52814.14  61262.00  61861.14  71784.71  59313.29
## [393]  61107.00  60603.43  60012.57  58280.43  56862.71  41704.43  51533.00
## [400]  50388.71  49205.29  56533.29  47996.14  47207.57  45292.00  40343.43
## [407]  39004.86  36788.43  30027.57  39040.14  42390.14  36291.14  30668.29
## [414]  47693.00  52094.43  56592.57  47971.43  43762.43  42246.71  46352.43
## [421]  33094.86  32784.86  26212.43  32611.57  42144.86  50034.86  46332.00
## [428]  42976.29  39456.29  39328.29  35296.14  30875.43  27709.00  29513.29
## [435]  31630.43  29346.14  34916.86  42020.86  38303.00  37966.43  41408.14
## [442]  38988.14  43555.29  38114.00  27847.86  26517.00  39518.29  39153.71
## [449]  45623.14  40627.43  41027.71  42882.86  47139.43  35547.57  41099.00
## [456]  35859.57  44524.57  48554.29  51554.29  47810.29  50490.00  50720.71
## [463]  52720.71  52145.57  55515.57  52457.00  58239.57  50523.57  47788.57
## [470]  46170.00  42305.57  46605.57  55149.57  48769.57  50719.43  44753.71
## [477]  42898.00  46141.14  34022.57  26651.86  28791.86  31879.00  33584.71
## [484]  34690.43  27410.43  41755.00  49379.57  57198.86  51144.57  56677.43
## [491]  65416.43  69779.71  54046.00  43259.57  40998.57  41368.57  42274.29
## [498]  35962.71  38709.00  44778.14  51282.43  52094.86  52221.43  45011.43
## [505]  46545.43  42263.00  45417.43  45034.71  37840.57  39135.43  38191.14
## [512]  39456.86  42479.14  34282.57  28878.43  56227.14  65569.43  69751.29
## [519]  62171.71  63705.14  79257.86  87244.71  58568.00  52695.29  48911.00
## [526]  53924.00  53358.86  42121.14  47835.71  62329.29  56056.86  59946.43
## [533]  64511.57  61137.43  55448.71  47964.43  46425.71  55512.00  55226.29
## [540]  46709.14  49254.71  49056.29  49850.57  39145.71  29799.43  34769.86
## [547]  44061.57  43829.14  45782.00  38924.57  49242.43  50565.00  38864.43
## [554]  49786.71  58787.86  58060.86  62179.43  57333.86  70797.00  89901.71
## [561]  78558.14  65466.00  70525.00  68377.86  69736.29  60085.86  41757.00
## [568]  49780.29  56540.29  57894.29  60270.29  61011.00  57721.43  71741.00
## [575]  59576.00  52390.29  61092.29  62814.00  54908.29  62082.00  57017.71
## [582]  53634.43  69169.00  52488.14  60895.57  59856.57  52670.00  51874.57
## [589]  52190.57  41562.43  44764.14  38612.71  43473.14  53505.00  45870.86
## [596]  52578.00  55300.00  61789.71  57391.71  62902.29  53250.43  55402.57
## [603]  56291.29  58933.57  59590.71  59065.00  52399.57  60483.43  58262.71
## [610]  54939.71  51169.00  43113.29  56289.71  60739.86  50363.14  62270.86
## [617]  67061.57  59609.00  85054.00  68023.29  59242.29  61535.14  56215.86
## [624]  45152.29  57409.57  35151.43  34991.43  45944.71  57944.71  55706.29
## [631]  88593.71  77359.43  79878.71  81753.00  75716.00  67381.43  63528.57
## [638]  49682.86  47815.00  46546.14  44808.71  42959.57  46023.86  51309.57
## [645]  68447.29  84959.29  81666.29  82700.86  89422.14 104812.71  98812.71
## [652]  64779.86  61862.86  58376.43  59503.57  55429.43  44454.57  47184.00
## [659]  52126.71  51202.00  64437.14  64297.14  64628.57  51413.14  52969.43
## [666]  54135.29  48799.43  41907.86  45382.00  42633.29  46624.71  44051.86
## [673]  35852.86  29737.71  29734.86  32881.71  38298.57  40886.14  38601.86
## [680]  38628.86  39142.57  32666.14  39911.57  39336.29  39678.86  41963.14
## [687]  54220.57  63901.86  73116.00  60863.86  56293.86  52725.00  55525.00
## [694]  44413.00  37200.14  30212.43  26456.71  24716.71  31020.29  32132.57
## [701]  32902.57
## 
## $interrupt_var
##   [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
##  [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
##  [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [112] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [149] 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [186] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [223] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [260] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [297] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [334] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [371] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [408] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [445] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [482] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [519] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [556] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [593] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [630] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [667] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## Levels: 0 1 2
## 
## $residuals
##            2            3            4            5            6            7 
##   1946.45276   4009.33353   -507.22691   2464.81139  -2908.63762    540.91572 
##            8            9           10           11           12           13 
##  -5623.25234  -1224.50025  -4006.64994   -497.06169  -5007.51239  -1724.68093 
##           14           15           16           17           18           19 
##  -1014.26436    272.64311  -3323.03543   -482.43361  -2219.58333   6505.57759 
##           20           21           22           23           24           25 
##  -1525.07678  -1217.49601   1458.84693  -1175.95017    235.51215   1705.35313 
##           26           27           28           29           30           31 
##  -7064.87300    896.70100   8166.72249    506.45652     75.19003  -2316.07365 
##           32           33           34           35           36           37 
##   1626.35292   4643.07475   1253.35254   2523.00738  -1715.91418   4723.91866 
##           38           39           40           41           42           43 
##   4372.16877  -2160.45534  -2908.99113  -1084.06694 -10732.16977   7161.80143 
##           44           45           46           47           48           49 
##   2538.76317   1383.66470   8137.75270    817.98284   6653.23731   6906.54521 
##           50           51           52           53           54           55 
##  -5628.68842  -4647.79437  -4990.93396  -7932.03030   6026.82032  -4088.33454 
##           56           57           58           59           60           61 
##  -4955.71347   3740.64438    836.61532    -65.08403    113.54315  -5019.15626 
##           62           63           64           65           66           67 
##  18044.11250   3798.81324  -3461.15876   6041.36604   7521.18632  14887.48597 
##           68           69           70           71           72           73 
##   2096.78858 -12837.84715  -1145.31278   4768.19504  -4731.79490  -4318.71475 
##           74           75           76           77           78           79 
## -10477.55158   2352.11215  -5467.99159    936.55456  -6963.23034    376.03660 
##           80           81           82           83           84           85 
##  -2496.25855  -2846.74468  -4098.82214   -732.40670   2138.21790   3638.43897 
##           86           87           88           89           90           91 
##    416.46869   -530.80049    150.59435   4264.74373  -1140.58820   1156.28652 
##           92           93           94           95           96           97 
##  -2044.61833  -1052.48825    157.78172    260.28419  -7492.67670   2290.27440 
##           98           99          100          101          102          103 
##  -8660.31452  -3098.99849  -4214.20120  -1939.55336  -1459.63275   2992.58323 
##          104          105          106          107          108          109 
##  -2465.80411   2456.98304  -1244.77627    880.99043   2521.77383  -3178.21679 
##          110          111          112          113          114          115 
##  -4783.08137   -961.68353   1796.08426  11624.32228  -1154.89469   2730.07042 
##          116          117          118          119          120          121 
##   4350.92433   3634.10658   -940.21956  -4589.98419  -3672.55656   2318.47639 
##          122          123          124          125          126          127 
##  -1703.82413   1344.41183   8879.31789    978.00732    256.19050  -2409.08409 
##          128          129          130          131          132          133 
##   2721.92321   7145.11890   1183.04688  -8336.89671   1784.52502   4189.08716 
##          134          135          136          137          138          139 
##  -3064.30930  -1371.98424   -829.55047  -3868.81105   1144.49046   -513.59819 
##          140          141          142          143          144          145 
##  -2935.06400   1663.26281  -1906.74735  -7874.86023   1901.44606  -3573.96949 
##          146          147          148          149          150          151 
##   1976.52473   -340.23578    948.00304   -411.40912   1302.66767   1160.96076 
##          152          153          154          155          156          157 
##   3349.66290  -4824.92341  -1203.04739  -3275.02848   5882.18970   9757.25284 
##          158          159          160          161          162          163 
##  -3477.97183  -4848.31527   3496.23153    159.57843   2681.27199  -5876.47578 
##          164          165          166          167          168          169 
##  -6778.70260   4057.98796  17371.25901   3832.56211   -163.62170  -2233.92419 
##          170          171          172          173          174          175 
##   -937.06864   3734.69752    -46.69195  -7908.05881   2922.64925   4423.47635 
##          176          177          178          179          180          181 
##    775.36133   8900.25528  -8999.75594  -3356.04051 -10674.63537 -11298.80540 
##          182          183          184          185          186          187 
##   1057.85139   9165.06453  -1417.81811   5932.73704   6640.83519  13320.91415 
##          188          189          190          191          192          193 
##   8738.29892  -3687.48618   2749.82068  10652.70213  -1266.61000  -2129.52419 
##          194          195          196          197          198          199 
## -10028.70791  -6258.97179   1256.64173  -5190.30881  -9812.06968   5262.61643 
##          200          201          202          203          204          205 
##  -3100.14040  -1767.11058   -862.64132   6443.08652   9921.15351    734.02614 
##          206          207          208          209          210          211 
##   3073.59527   3268.68433   5976.23522  13075.40603  -5319.83867 -11035.24493 
##          212          213          214          215          216          217 
##  -5563.26622 -10555.26616  -5161.89557   1401.51298 -13092.12346  16176.12633 
##          218          219          220          221          222          223 
##   7823.67368   1639.16307  26810.19850  12947.42426   7851.88392  14567.07386 
##          224          225          226          227          228          229 
##  -3275.29953  -1224.71897   4213.14310    790.42237   3133.06967   9382.25142 
##          230          231          232          233          234          235 
##   6274.15258  -1440.62256  -1434.88253   9756.12108 -11101.88793  -7055.11208 
##          236          237          238          239          240          241 
##  -8418.87577 -10086.14057   2981.85571   1313.70497  -8305.63284  -9084.38198 
##          242          243          244          245          246          247 
##   8916.95385  -7805.34778   2371.42708 -10364.15170  -4219.21124   1240.85196 
##          248          249          250          251          252          253 
##    869.99786 -12411.54411   3426.31197   1924.07003   4123.15272   2113.79526 
##          254          255          256          257          258          259 
##  -1146.80975  11145.12742  21019.76083   3559.09194  -3916.01240   4374.70197 
##          260          261          262          263          264          265 
##  -1412.24917   3968.76955  -4602.56765 -10726.38882  -4700.75435   -544.75994 
##          266          267          268          269          270          271 
##  -5207.91151   8708.40993  -4237.09956   4183.79992  -2060.10120   4452.60410 
##          272          273          274          275          276          277 
##    781.80454   7378.27306  -1261.92757  12143.12957  -4347.02837   1882.41892 
##          278          279          280          281          282          283 
##   -214.49063   7987.01761  -4851.24056  -2604.09925 -11176.42130  -2712.23352 
##          284          285          286          287          288          289 
##  18594.42173   7937.07946   2954.15170   -405.63879   1095.41970   6575.10733 
##          290          291          292          293          294          295 
##   7107.04321 -18501.43831 -11097.99094  -8196.16035   9522.99040   3059.01901 
##          296          297          298          299          300          301 
##  -1151.10778  27420.51822  10373.27307   5276.48689   9898.29888   3289.12265 
##          302          303          304          305          306          307 
##   -624.00000   8243.82766 -23907.82893  -3442.66659   -123.38520  -6916.27244 
##          308          309          310          311          312          313 
##  -3988.93746   2886.89948  -9191.06160  -3311.73931  -8277.52660   1416.10024 
##          314          315          316          317          318          319 
##  -3251.92288   1941.12634  -4143.29733  27362.12085   -529.86184   3456.93282 
##          320          321          322          323          324          325 
##  11012.08151   5860.21546  32676.44629   5698.91242 -20369.75225   2081.24875 
##          326          327          328          329          330          331 
##   1385.92766  -6207.82666  -1567.93281 -33132.10083    714.78050  -2417.36586 
##          332          333          334          335          336          337 
##   -194.94842  -3236.56938   4014.96716   -439.01516  -6939.66020  -3153.87759 
##          338          339          340          341          342          343 
##  -2234.43022  -7717.87283   3764.11046  -1390.43396  -1748.58194  -1000.79694 
##          344          345          346          347          348          349 
##    180.23661    505.32874  -1575.13358  -9406.88367 -13248.92590   2167.89544 
##          350          351          352          353          354          355 
##  -4404.91045  -3751.33973  -6075.77142   1630.16412   1315.27227   2723.87855 
##          356          357          358          359          360          361 
##  -3749.65094   -521.58632    683.97617   7041.52044    381.29755     69.36488 
##          362          363          364          365          366          367 
##   2690.26948  -2618.87080   -773.78976  -8645.63052  -4603.98227  -6216.88241 
##          368          369          370          371          372          373 
##  -4990.84433  -7313.84232   4915.39968    348.69326   7121.12254  -7553.75441 
##          374          375          376          377          378          379 
##  -2243.60448  -3373.02878  -2465.90058 -12459.57772   1807.16424 -10679.98706 
##          380          381          382          383          384          385 
##   5579.10903   9304.99522   3199.78478  -2291.75664   1690.42826   6847.50800 
##          386          387          388          389          390          391 
##  11573.98171  -5548.63753  -5188.60572    -45.68971   8669.62551   1993.63799 
##          392          393          394          395          396          397 
##  11401.23923  -9616.17618   2917.69000    869.40669    712.21547   -511.09270 
##          398          399          400          401          402          403 
##   -437.11922 -14374.49694   8508.09553  -1100.36487  -1298.35683   7048.78886 
##          404          405          406          407          408          409 
##  -7799.08518  -1235.63510  -2472.10414  -5771.02269  -2847.98058  -3911.65745 
##          410          411          412          413          414          415 
##  -8763.76939   6071.12081   1659.66946  -7324.28543  -7694.80231  14172.20727 
##          416          417          418          419          420          421 
##   3912.28278   4620.00090  -7874.85517  -4659.49449  -2550.49978   2860.51818 
##          422          423          424          425          426          427 
## -13932.81459  -2825.65174  -9131.11434   2928.07626   6950.54452   6630.66415 
##          428          429          430          431          432          433 
##  -3866.90765  -4033.79333  -4663.91741  -1760.56182  -5682.47357  -6630.78479 
##          434          435          436          437          438          439 
##  -5990.18014  -1459.02768   -895.70061  -5003.22861   2534.66814   4841.27727 
##          440          441          442          443          444          445 
##  -4994.40660  -2129.23172   1602.33134  -3781.60611   2869.59371  -6504.82667 
##          446          447          448          449          450          451 
## -12085.04002  -4574.89447   9572.49955  -1988.52644   4794.86396  -5772.19648 
##          452          453          454          455          456          457 
##  -1069.69872    440.72586   3099.68432 -12157.84941   3376.26124  -6643.94972 
##          458          459          460          461          462          463 
##   6533.14433   3100.72903   2630.41741  -3697.12429   2206.85003    129.84646 
##          464          465          466          467          468          469 
##   1931.15980   -366.34419   3498.95766  -2461.79227   5954.76143  -6741.07670 
##          470          471          472          473          474          475 
##  -2831.20746  -2094.45005  -4564.99636   3062.97441   7903.89799  -5834.02875 
##          476          477          478          479          480          481 
##   1610.16039  -6034.73297  -2752.89006   2088.35787 -12823.14515  -9757.58126 
##          482          483          484          485          486          487 
##  -1270.06537    -25.84892   -978.72206  -1341.93577  -9574.15542  11039.81052 
##          488          489          490          491          492          493 
##   6311.11583   7564.26882  -5223.83378   5522.85655   9497.06751   6334.49627 
##          494          495          496          497          498          499 
## -13156.79487 -10393.65449  -3365.59184  -1048.46259   -461.38511  -7552.94008 
##          500          501          502          503          504          505 
##    628.74828   4332.84326   5610.50110    821.56523    248.48896  -7070.51189 
##          506          507          508          509          510          511 
##    672.59998  -4930.87958   1911.49324  -1187.75362  -8052.31037   -561.99722 
##          512          513          514          515          516          517 
##  -2621.38874   -542.47395   1389.80322  -9409.50193  -7754.91677  24247.74031 
##          518          519          520          521          522          523 
##  10037.84482   6174.30708  -5006.59845   3054.20973  17286.36513  11879.52700 
##          524          525          526          527          528          529 
## -23675.31342  -4852.19872  -3579.01703   4692.93628   -189.30475 -10940.32899 
##          530          531          532          533          534          535 
##   4451.94357  14024.23715  -4729.77344   4561.49156   5777.01110  -1528.54406 
##          536          537          538          539          540          541 
##  -4311.51209  -6896.78763  -1990.18052   8421.21603    310.56909  -7960.52218 
##          542          543          544          545          546          547 
##   1919.84714   -470.77807    494.39048 -10894.49009 -11021.96088   1997.30729 
##          548          549          550          551          552          553 
##   7008.58510  -1225.68723    927.33288  -7611.85830   8611.48305   1048.51644 
##          554          555          556          557          558          559 
## -11791.02780   9207.56262   8802.64530    324.03602   5068.68571  -3323.71938 
##          560          561          562          563          564          565 
##  14312.33541  21822.85273  -5973.32733  -9296.60694   7037.09204    533.23671 
##          566          567          568          569          570          571 
##   3740.74297  -7079.53788 -17097.63350   6710.10775   6560.61003   2093.02942 
##          572          573          574          575          576          577 
##   3302.99093   1997.54020  -1929.91998  14922.56643  -9315.81600  -6025.26872 
##          578          579          580          581          582          583 
##   8864.92876   3092.64977  -6295.77139   7686.19038  -3555.95865  -2577.97993 
##          584          585          586          587          588          589 
##  15870.21140 -14188.71674   8583.94180    304.62772  -5987.17710   -593.67003 
##          590          591          592          593          594          595 
##    407.33759 -10492.93832   1861.52603  -7047.15666   3110.76264   8956.91316 
##          596          597          598          599          600          601 
##  -7316.47009   5965.04822   2910.98715   7056.56794  -2930.24787   6367.79568 
##          602          603          604          605          606          607 
##  -8029.65848   2434.47616   1469.80685   3346.74909   1728.40685    636.77390 
##          608          609          610          611          612          613 
##  -5575.91974   8248.07494   -934.30000  -2344.86402  -3253.87529  -8062.32372 
##          614          615          616          617          618          619 
##  12051.52943   5154.38809  -9054.70273  11789.23622   6325.25840  -5252.98307 
##          620          621          622          623          624          625 
##  26610.02660 -12333.42714  -6447.90700   3406.97668  -3886.87305 -10369.57900 
##          626          627          628          629          630          631 
##  11415.43940 -21398.43969  -2390.15966   8700.91495  11268.15817  -1304.43717 
##          632          633          634          635          636          637 
##  33510.68258  -6045.61743   6148.41679   5853.14261  -1797.95581  -4933.58018 
##          638          639          640          641          642          643 
##  -1608.86639 -12136.57499  -2080.77209  -1741.06696  -2385.78042  -2738.68356 
##          644          645          646          647          648          649 
##   1918.04804   4564.85902  17150.61890  18903.95244   1391.13896   5261.57799 
##          650          651          652          653          654          655 
##  11091.91089  20694.24170   1440.18071 -27425.59305  -1034.15793  -2008.52279 
##          656          657          658          659          660          661 
##   2121.06459  -2923.75179 -10390.03629   1790.72595   4382.90940   -798.37436 
##          662          663          664          665          666          667 
##  13233.11446   1695.26667   2147.26052 -11353.58790   1583.56805   1409.18219 
##          668          669          670          671          672          673 
##  -4930.68847  -7227.12343   2181.90680  -3558.67179   2799.89610  -3210.30175 
##          674          675          676          677          678          679 
##  -9193.60719  -8247.93060  -2984.54556    164.77210   2871.61703    794.29636 
##          680          681          682          683          684          685 
##  -3718.35557  -1724.17311  -1233.71070  -8152.54003   4670.26295  -2144.64503 
##          686          687          688          689          690          691 
##  -1306.64872    682.62113  10972.86725  10098.29370  10975.10147  -9212.08214 
##          692          693          694          695          696          697 
##  -3230.77484  -2864.03680   3009.39361 -10513.91197  -8157.33068  -8933.47258 
##          698          699          700          701          702 
##  -6671.50314  -5177.15499   2624.87062  -1691.35686  -1879.23570 
## 
## $fitted.values
##        2        3        4        5        6        7        8        9 
## 17322.83 20129.67 24323.37 24045.33 26365.35 23735.80 24441.97 19741.64 
##       10       11       12       13       14       15       16       17 
## 19481.94 16862.35 17628.80 14404.54 14454.98 15110.21 16782.75 15126.58 
##       18       19       20       21       22       23       24       25 
## 16146.58 15528.99 22511.08 21608.07 21095.30 22958.52 22294.06 22937.36 
##       26       27       28       29       30       31       32       33 
## 24757.16 18771.58 20473.28 28199.54 28256.38 27933.93 25596.93 26979.50 
##       34       35       36       37       38       39       40       41 
## 30768.08 31111.56 32500.77 30046.65 34070.83 37233.46 34331.28 31187.35 
##       42       43       44       45       46       47       48       49 
## 30051.46 20764.48 28176.67 30578.62 31652.39 38393.59 37895.33 42491.45 
##       50       51       52       53       54       55       56       57 
## 46667.69 39469.08 34114.51 29207.74 22449.32 28650.19 25279.28 21629.36 
##       58       59       60       61       62       63       64       65 
## 25975.24 27216.94 27509.74 27915.73 23845.17 40201.33 42019.16 37332.49 
##       66       67       68       69       70       71       72       73 
## 41479.81 46325.80 56842.78 54884.70 40337.03 37878.23 40853.37 35234.29 
##       74       75       76       77       78       79       80       81 
## 30750.98 21586.17 24742.28 20725.73 22782.23 17750.11 19736.97 18974.46 
##       82       83       84       85       86       87       88       89 
## 18015.96 16112.26 17371.92 20928.85 25283.96 26259.80 26284.41 26892.40 
##       90       91       92       93       94       95       96       97 
## 30959.02 29806.14 30791.33 28883.20 28094.36 28457.29 28858.11 22526.58 
##       98       99      100      101      102      103      104      105 
## 25498.89 18628.14 17500.49 15568.98 15864.49 16532.27 20941.52 20038.02 
##      106      107      108      109      110      111      112      113 
## 23499.35 23292.30 24944.65 27780.65 25314.22 21808.11 22079.63 24688.39 
##      114      115      116      117      118      119      120      121 
## 35398.89 33617.36 35428.79 38384.61 40312.79 38033.98 32928.41 29321.67 
##      122      123      124      125      126      127      128      129 
## 31374.97 29679.30 30844.11 38336.14 37983.67 37058.51 33966.51 35722.45 
##      130      131      132      133      134      135      136      137 
## 41043.81 40492.04 31818.47 33065.34 36209.88 32671.41 31081.55 30179.53 
##      138      139      140      141      142      143      144      145 
## 26785.37 28179.74 27952.64 25671.74 27667.46 26311.72 20004.55 22992.11 
##      146      147      148      149      150      151      152      153 
## 20849.62 23784.52 24316.85 25884.69 26064.19 27694.90 28977.19 31966.35 
##      154      155      156      157      158      159      160      161 
## 27500.76 26774.17 24364.10 30174.60 41498.40 39852.32 37254.63 42203.71 
##      162      163      164      165      166      167      168      169 
## 43592.30 46959.76 42489.99 37863.73 43212.03 59283.01 61463.76 59900.35 
##      170      171      172      173      174      175      176      177 
## 56771.07 55193.02 57857.26 56895.20 49296.64 52080.10 55769.64 55805.32 
##      178      179      180      181      182      183      184      185 
## 62833.04 53470.04 50267.06 41206.09 32865.43 36323.94 46284.10 45747.83 
##      186      187      188      189      190      191      192      193 
## 51616.16 57279.66 67909.70 73117.63 66901.75 67092.44 74062.47 69800.24 
##      194      195      196      197      198      199      200      201 
## 65386.57 54782.97 48897.79 50301.88 45959.07 38238.95 44572.57 42825.11 
##      202      203      204      205      206      207      208      209 
## 42468.21 42939.77 49637.42 58400.55 58035.40 59735.74 61368.05 65105.45 
##      210      211      212      213      214      215      216      217 
## 74437.70 66632.82 54989.41 49674.69 40798.75 37799.63 40869.12 31030.87 
##      218      219      220      221      222      223      224      225 
## 47763.61 54980.55 55869.66 78312.15 85700.83 87675.64 95159.30 86238.58 
##      226      227      228      229      230      231      232      233 
## 80322.14 79910.01 76607.50 75780.89 80450.70 81795.62 76310.03 71590.88 
##      234      235      236      237      238      239      240      241 
## 77164.32 64001.54 56151.02 48215.85 39946.43 44078.87 46201.06 39744.67 
##      242      243      244      245      246      247      248      249 
## 33513.90 43650.49 37979.00 41858.87 34232.50 32956.72 36560.14 39343.97 
##      250      251      252      253      254      255      256      257 
## 30303.55 36157.36 39904.85 45025.92 47705.67 47205.44 57360.24 74609.19 
##      258      259      260      261      262      263      264      265 
## 74426.87 67832.44 69293.25 65567.66 66993.28 60839.53 50266.33 46350.05 
##      266      267      268      269      270      271      272      273 
## 46556.48 42718.45 51397.67 47723.63 51811.53 49954.82 53964.48 54256.30 
##      274      275      276      277      278      279      280      281 
## 60188.36 57856.16 67391.89 61402.87 61609.92 59982.41 65643.81 59463.24 
##      282      283      284      285      286      287      288      289 
## 56075.85 45776.38 44195.86 61183.63 66635.28 67038.92 64493.15 63593.46 
##      290      291      292      293      294      295      296      297 
## 67537.67 71392.44 52658.56 42901.02 36997.01 47171.98 50367.82 49494.34 
##      298      299      300      301      302      303      304      305 
## 73347.44 79208.51 79866.70 84413.73 82637.86 77738.60 81156.26 56411.10 
##      306      307      308      309      310      311      312      313 
## 52725.24 52409.56 46287.79 43536.81 47089.06 39746.88 38487.10 33125.76 
##      314      315      316      317      318      319      320      321 
## 36856.64 36049.59 39826.73 37839.74 63260.43 61132.21 62732.78 70617.50 
##      322      323      324      325      326      327      328      329 
## 72970.98 98091.37 96492.04 72664.89 71479.79 69860.40 61926.22 59089.24 
##      330      331      332      333      334      335      336      337 
## 29463.65 33098.94 33532.23 35819.28 35169.46 40854.73 41915.09 37230.02 
##      338      339      340      341      342      343      344      345 
## 36455.57 36580.44 31965.75 37879.72 38533.72 38788.51 39651.91 41412.53 
##      346      347      348      349      350      351      352      353 
## 43208.71 42963.88 36008.50 26709.96 31978.91 30856.05 30451.91 28102.12 
##      354      355      356      357      358      359      360      361 
## 32714.73 36415.84 40816.22 39030.87 40273.31 42381.48 49671.99 50214.78 
##      362      363      364      365      366      367      368      369 
## 50413.59 52841.87 50360.93 49813.34 42562.70 39799.17 36030.27 33840.41 
##      370      371      372      373      374      375      376      377 
## 29954.03 37138.74 39393.31 47167.18 41224.18 40679.17 39237.19 38776.58 
##      378      379      380      381      382      383      384      385 
## 29773.55 34306.56 27456.61 35559.58 45746.36 49261.33 47559.14 49522.63 
##      386      387      388      389      390      391      392      393 
## 55654.73 65005.92 58313.32 52859.83 52592.37 59867.50 60383.48 68929.46 
##      394      395      396      397      398      399      400      401 
## 58189.31 59734.02 59300.36 58791.52 57299.83 56078.93 43024.90 51489.08 
##      402      403      404      405      406      407      408      409 
## 50503.64 49484.50 55795.23 48443.21 47764.10 46114.45 41852.84 40700.09 
##      410      411      412      413      414      415      416      417 
## 38791.34 32969.02 40730.47 43615.43 38363.09 33520.79 48182.15 51972.57 
##      418      419      420      421      422      423      424      425 
## 55846.28 48421.92 44797.21 43491.91 47027.67 35610.51 35343.54 29683.50 
##      426      427      428      429      430      431      432      433 
## 35194.31 43404.19 50198.91 47010.08 44120.20 41088.85 40978.62 37506.21 
##      434      435      436      437      438      439      440      441 
## 33699.18 30972.31 32526.13 34349.37 32382.19 37179.58 43297.41 40095.66 
##      442      443      444      445      446      447      448      449 
## 39805.81 42769.75 40685.69 44618.83 39932.90 31091.89 29945.79 41142.24 
##      450      451      452      453      454      455      456      457 
## 40828.28 46399.63 42097.41 42442.13 44039.74 47705.42 37722.74 42503.52 
##      458      459      460      461      462      463      464      465 
## 37991.43 45453.56 48923.87 51507.41 48283.15 50590.87 50789.55 52511.92 
##      466      467      468      469      470      471      472      473 
## 52016.61 54918.79 52284.81 57264.65 50619.78 48264.45 46870.57 43542.60 
##      474      475      476      477      478      479      480      481 
## 47245.67 54603.60 49109.27 50788.45 45650.89 44052.78 46845.72 36409.44 
##      482      483      484      485      486      487      488      489 
## 30061.92 31904.85 34563.44 36032.36 36984.58 30715.19 43068.46 49634.59 
##      490      491      492      493      494      495      496      497 
## 56368.41 51154.57 55919.36 63445.22 67202.79 53653.23 44364.16 42417.03 
##      498      499      500      501      502      503      504      505 
## 42735.67 43515.65 38080.25 40445.30 45671.93 51273.29 51972.94 52081.94 
##      506      507      508      509      510      511      512      513 
## 45872.83 47193.88 43505.94 46222.47 45892.88 39697.43 40812.53 39999.33 
##      514      515      516      517      518      519      520      521 
## 41089.34 43692.07 36633.35 31979.40 55531.58 63576.98 67178.31 60650.93 
##      522      523      524      525      526      527      528      529 
## 61971.49 75365.19 82243.31 57547.48 52490.02 49231.06 53548.16 53061.47 
##      530      531      532      533      534      535      536      537 
## 43383.77 48305.05 60786.63 55384.94 58734.56 62665.97 59760.23 54861.22 
##      538      539      540      541      542      543      544      545 
## 48415.89 47090.78 54915.72 54669.67 47334.87 49527.06 49356.18 50040.20 
##      546      547      548      549      550      551      552      553 
## 40821.39 32772.55 37052.99 45054.83 44854.67 46536.43 40630.95 49516.48 
##      554      555      556      557      558      559      560      561 
## 50655.46 40579.15 49985.21 57736.82 57110.74 60657.58 56484.66 68078.86 
##      562      563      564      565      566      567      568      569 
## 84531.47 74762.61 63487.91 67844.62 65995.54 67165.40 58854.63 43070.18 
##      570      571      572      573      574      575      576      577 
## 49979.68 55801.26 56967.29 59013.46 59651.35 56818.43 68891.82 58415.55 
##      578      579      580      581      582      583      584      585 
## 52227.36 59721.35 61204.06 54395.81 60573.67 56212.41 53298.79 66676.86 
##      586      587      588      589      590      591      592      593 
## 52311.63 59551.94 58657.18 52468.24 51783.23 52055.37 42902.62 45659.87 
##      594      595      596      597      598      599      600      601 
## 40362.38 44548.09 53187.33 46612.95 52389.01 54733.15 60321.96 56534.49 
##      602      603      604      605      606      607      608      609 
## 61280.09 52968.10 54821.48 55586.82 57862.31 58428.23 57975.49 52235.35 
##      610      611      612      613      614      615      616      617 
## 59197.01 57284.58 54422.88 51175.61 44238.18 55585.47 59417.85 50481.62 
##      618      619      620      621      622      623      624      625 
## 60736.31 64861.98 58443.97 80356.71 65690.19 58128.17 60102.73 55521.86 
##      626      627      628      629      630      631      632      633 
## 45994.13 56549.87 37381.59 37243.80 46676.56 57010.72 55083.03 83405.05 
##      634      635      636      637      638      639      640      641 
## 73730.30 75899.86 77513.96 72315.01 65137.44 61819.43 49895.77 48287.21 
##      642      643      644      645      646      647      648      649 
## 47194.49 45698.25 44105.81 46744.71 51296.67 66055.33 80275.15 77439.28 
##      650      651      652      653      654      655      656      657 
## 78330.23 84118.47 97372.53 92205.45 62897.02 60384.95 57382.51 58353.18 
##      658      659      660      661      662      663      664      665 
## 54844.61 45393.27 47743.80 52000.37 51204.03 62601.88 62481.31 62766.73 
##      666      667      668      669      670      671      672      673 
## 51385.86 52726.10 53730.12 49134.98 43200.09 46191.96 43824.82 47262.16 
##      674      675      676      677      678      679      680      681 
## 45046.46 37985.64 32719.40 32716.94 35426.95 40091.85 42320.21 40353.03 
##      682      683      684      685      686      687      688      689 
## 40376.28 40818.68 35241.31 41480.93 40985.51 41280.52 43247.70 53803.56 
##      690      691      692      693      694      695      696      697 
## 62140.90 70075.94 59524.63 55589.04 52515.61 54926.91 45357.47 39145.90 
##      698      699      700      701      702 
## 33128.22 29893.87 28395.42 33823.93 34781.81 
## 
## $shapiro.test
## [1] 0
## 
## $levenes.test
## [1] 0
## 
## $autcorr
## [1] "No autocorrelation evidence"
## 
## $post_sums
## [1] "Post-Est Warning"
## 
## $adjr_sq
## [1] 0.8243
## 
## $fstat.bootstrap
## 
## ORDINARY NONPARAMETRIC BOOTSTRAP
## 
## 
## Call:
## boot::boot(data = x, statistic = f.stat, R = Reps, formula = depvar ~ 
##     ., parallel = parr)
## 
## 
## Bootstrap Statistics :
##        original     bias    std. error
## t1*    7.604798  0.5064645    3.407559
## t2* 1977.696325 24.2632003  231.692657
## WARNING: All values of t3* are NA
## 
## $itsa.plot
## 
## $booted.ints
##       Parameter    Lower CI Median F-value  Upper CI
## 1 interrupt_var    3.157064       7.771773   14.2939
## 2    lag_depvar 1644.430551    1987.951911 2402.6800

Ahora con las tendencias descompuestas

require(zoo)
require(scales)
Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha2=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(gastador=ifelse(gastador=="Andrés",1,0)) %>%
    dplyr::mutate(treat=ifelse(fecha2>"2019-W26",1,0)) %>% 
   dplyr::mutate(gasto= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
    gasto=="aspiradora"~"electrodomésticos/mantención casa",
                                            gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
                                            gasto=="Tina"~"electrodomésticos/mantención casa",
                                            gasto=="Nexium"~"Farmacia",
                                            gasto=="donaciones"~"donaciones/regalos",
                                            gasto=="Regalo chocolates"~"donaciones/regalos",
                                            gasto=="filtro piscina msp"~"electrodomésticos/mantención casa",
                                            gasto=="Chromecast"~"electrodomésticos/mantención casa",
                                            gasto=="Muebles ratan"~"electrodomésticos/mantención casa",
                                            gasto=="Vacuna Influenza"~"Farmacia",
                                            gasto=="Easy"~"electrodomésticos/mantención casa",
                                            gasto=="Sopapo"~"electrodomésticos/mantención casa",
                                            gasto=="filtro agua"~"electrodomésticos/mantención casa",
                                            gasto=="ropa tami"~"donaciones/regalos",
                                            gasto=="yaz"~"Farmacia",
                                            gasto=="Yaz"~"Farmacia",
                                            gasto=="Remedio"~"Farmacia",
                                            gasto=="Entel"~"VTR",
                                            gasto=="Kerosen"~"Gas/Bencina",
                                            gasto=="Parafina"~"Gas/Bencina",
                                            gasto=="Plata basurero"~"donaciones/regalos",
                                            gasto=="Matri Andrés Kogan"~"donaciones/regalos",
                                            gasto=="Wild Protein"~"Comida",
                                            gasto=="Granola Wild Foods"~"Comida",
                                            gasto=="uber"~"Transporte",
                                            gasto=="Uber Reñaca"~"Transporte",
                                            gasto=="filtro piscina mspa"~"electrodomésticos/mantención casa",
                                            gasto=="Limpieza Alfombra"~"electrodomésticos/mantención casa",
                                            gasto=="Aspiradora"~"electrodomésticos/mantención casa",
                                            gasto=="Limpieza alfombras"~"electrodomésticos/mantención casa",
                                            gasto=="Pila estufa"~"electrodomésticos/mantención casa",
                                            gasto=="Reloj"~"electrodomésticos/mantención casa",
                                            gasto=="Arreglo"~"electrodomésticos/mantención casa",
                                            gasto=="Pan Pepperino"~"Comida",
                                            gasto=="Cookidoo"~"Comida",
                                            gasto=="remedios"~"Farmacia",
                                            gasto=="Bendina Reñaca"~"Gas/Bencina",
                                            gasto=="Bencina Reñaca"~"Gas/Bencina",
                                            gasto=="Vacunas Influenza"~"Farmacia",
                                            gasto=="Remedios"~"Farmacia",
                                            gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
                                        T~gasto)) %>% 
    dplyr::group_by(gastador, fecha,gasto, .drop=F) %>%
    #dplyr::mutate(fecha_simp=week(parse_date(fecha))) %>% 
#    dplyr::mutate(fecha_simp=tsibble::yearweek(fecha)) %>%#después de  diosi. Junio 24, 2019   
    dplyr::summarise(monto=sum(monto)) %>% 
    dplyr::mutate(gastador_nombre=plyr::revalue(as.character(gastador), c("0" = "Tami", "1"="Andrés"))) %>% 
  ggplot2::ggplot(aes(x = fecha, y = monto, color=as.factor(gastador_nombre))) +
  #stat_summary(geom = "line", fun.y = median, size = 1, alpha=0.5, aes(color="blue")) +
  geom_line(size=1) +
  facet_grid(gasto~.)+
  #geom_text(aes(x = fech_ing_qrt, y = perc_dup-0.05, label = paste0(n)), vjust = -1,hjust = 0, angle=45, size=3) +

  geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
  labs(y="Gastos (en miles)",x="Semanas y Meses", subtitle="Interlineado, incorporación de la Diosi; Azul= Tami; Rojo= Andrés") +
  ggtitle( "Figura 6. Gastos Semanales por Gastador e ítem (media)") +
  scale_y_continuous(labels = f <- function(x) paste0(x/1000)) + 
  scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
  scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
  guides(color = F)+
  theme_custom() +
  theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
  theme(
    panel.border = element_blank(), 
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(), 
    axis.line = element_line(colour = "black")
    )

autoplot(forecast::mstl(Gastos_casa$monto, lambda = "auto",iterate=5000000,start = 
lubridate::decimal_date(as.Date("2019-03-03"))))

 # scale_x_continuous(breaks = seq(0,400,by=30))
msts <- forecast::msts(Gastos_casa$monto,seasonal.periods = c(7,30.5,365.25),start = 
lubridate::decimal_date(as.Date("2019-03-03")))
#tbats <- forecast::tbats(msts,use.trend = FALSE)
#plot(tbats, main="Multiple Season Decomposition")
library(bsts)
library(CausalImpact)
ts_week_covid<-  
Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_week=strftime(fecha, format = "%Y-W%V")) %>%
    dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
    dplyr::group_by(fecha_week)%>%
    dplyr::summarise(gasto_total=sum(monto,na.rm=T)/1000,min_day=min(day))%>%
    dplyr::ungroup() %>% 
    dplyr::mutate(covid=dplyr::case_when(min_day>=as.Date("2020-03-17")~1,TRUE~0))%>%
    dplyr::mutate(covid=as.factor(covid))%>%
    data.frame()


ts_week_covid$gasto_total_na<-ts_week_covid$gasto_total
post_resp<-ts_week_covid$gasto_total[which(ts_week_covid$covid==1)]
ts_week_covid$gasto_total_na[which(ts_week_covid$covid==1)]<-NA
ts_week_covid$gasto_total[which(ts_week_covid$covid==0)]
##  [1]  98.357   4.780  56.784  50.506  64.483  67.248  49.299  35.786  58.503
## [10]  64.083  20.148  73.476 127.004  81.551  69.599 134.446  58.936  26.145
## [19] 129.927 104.989 130.860  81.893  95.697  64.579 303.471 151.106  49.275
## [28]  76.293  33.940  83.071 119.512  20.942  58.055  71.728  44.090  33.740
## [37]  59.264  77.410  60.831  63.376  48.754 235.284  29.604 115.143  72.419
## [46]   5.980  80.063 149.178  69.918 107.601  72.724  63.203  99.681 130.309
## [55] 195.898 112.066
# Model 1
ssd <- list()
# Local trend, weekly-seasonal #https://qastack.mx/stats/209426/predictions-from-bsts-model-in-r-are-failing-completely - PUSE UN GENERALIZED LOCAL TREND
ssd <- AddLocalLevel(ssd, ts_week_covid$gasto_total_na) #AddSemilocalLinearTrend #AddLocalLevel
# Add weekly seasonal
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na,nseasons=5, season.duration = 52) #weeks OJO, ESTOS NO SON WEEKS VERDADEROS. PORQUE TENGO MAS DE EUN AÑO
ssd <- AddSeasonal(ssd, ts_week_covid$gasto_total_na, nseasons = 12, season.duration =4) #years
# For example, to add a day-of-week component to data with daily granularity, use model.args = list(nseasons = 7, season.duration = 1). To add a day-of-week component to data with hourly granularity, set model.args = list(nseasons = 7, season.duration = 24).
model1d1 <- bsts(ts_week_covid$gasto_total_na, 
               state.specification = ssd, #A list with elements created by AddLocalLinearTrend, AddSeasonal, and similar functions for adding components of state. See the help page for state.specification.
               family ="student", #A Bayesian Analysis of Time-Series Event Count Data. POISSON NO SE PUEDE OCUPAR
               niter = 20000, 
               #burn = 200, #http://finzi.psych.upenn.edu/library/bsts/html/SuggestBurn.html Suggest the size of an MCMC burn in sample as a proportion of the total run.
               seed= 2125)
## =-=-=-=-= Iteration 0 Mon Apr 22 00:39:48 2024
##  =-=-=-=-=
## =-=-=-=-= Iteration 2000 Mon Apr 22 00:39:55 2024
##  =-=-=-=-=
## =-=-=-=-= Iteration 4000 Mon Apr 22 00:40:02 2024
##  =-=-=-=-=
## =-=-=-=-= Iteration 6000 Mon Apr 22 00:40:10 2024
##  =-=-=-=-=
## =-=-=-=-= Iteration 8000 Mon Apr 22 00:40:17 2024
##  =-=-=-=-=
## =-=-=-=-= Iteration 10000 Mon Apr 22 00:40:24 2024
##  =-=-=-=-=
## =-=-=-=-= Iteration 12000 Mon Apr 22 00:40:31 2024
##  =-=-=-=-=
## =-=-=-=-= Iteration 14000 Mon Apr 22 00:40:38 2024
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## =-=-=-=-= Iteration 16000 Mon Apr 22 00:40:45 2024
##  =-=-=-=-=
## =-=-=-=-= Iteration 18000 Mon Apr 22 00:40:52 2024
##  =-=-=-=-=
#,
#               dynamic.regression=T)
#plot(model1d1, main = "Model 1")
#plot(model1d1, "components")

impact2d1 <- CausalImpact(bsts.model = model1d1,
                       post.period.response = post_resp)
plot(impact2d1)+
xlab("Date")+
  ylab("Monto Semanal (En miles)")

burn1d1 <- SuggestBurn(0.1, model1d1)
corpus <- Corpus(VectorSource(Gastos_casa$obs)) # formato de texto
d  <- tm_map(corpus, tolower)
d  <- tm_map(d, stripWhitespace)
d <- tm_map(d, removePunctuation)
d <- tm_map(d, removeNumbers)
d <- tm_map(d, removeWords, stopwords("spanish"))
d <- tm_map(d, removeWords, "menos")
tdm <- TermDocumentMatrix(d)
m <- as.matrix(tdm) #lo vuelve una matriz
v <- sort(rowSums(m),decreasing=TRUE) #lo ordena y suma
df <- data.frame(word = names(v),freq=v) # lo nombra y le da formato de data.frame
#findFreqTerms(tdm)
#require(devtools)
#install_github("lchiffon/wordcloud2")
#wordcloud2::wordcloud2(v, size=1.2)
wordcloud(words = df$word, freq = df$freq, 
          max.words=100, random.order=FALSE, rot.per=0.35, 
          colors=brewer.pal(8, "Dark2"), main="Figura 7. Nube de Palabras, Observaciones")

fit_month_gasto <- Gastos_casa %>%
    dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
    dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
    dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))%>%
  dplyr::mutate(gasto2= dplyr::case_when(gasto=="Gas"~"Gas/Bencina",
    gasto=="aspiradora"~"electrodomésticos/mantención casa",
                                            gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
                                            gasto=="Tina"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Nexium"~"Farmacia",
                                            gasto=="donaciones"~"donaciones/regalos",
                                            gasto=="Regalo chocolates"~"donaciones/regalos",
                                            gasto=="filtro piscina msp"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Chromecast"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Muebles ratan"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Vacuna Influenza"~"Farmacia",
                                            gasto=="Easy"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Sopapo"~"Electrodomésticos/ Mantención casa",
                                            gasto=="filtro agua"~"Electrodomésticos/ Mantención casa",
                                            gasto=="ropa tami"~"donaciones/regalos",
                                            gasto=="yaz"~"Farmacia",
                                            gasto=="Yaz"~"Farmacia",
                                            gasto=="Remedio"~"Farmacia",
                                            gasto=="Entel"~"VTR",
                                            gasto=="Kerosen"~"Gas/Bencina",
                                            gasto=="Parafina"~"Gas/Bencina",
                                            gasto=="Plata basurero"~"donaciones/regalos",
                                            gasto=="Matri Andrés Kogan"~"donaciones/regalos",
                                            gasto=="Wild Protein"~"Comida",
                                            gasto=="Granola Wild Foods"~"Comida",
                                            gasto=="uber"~"Otros",
                                            gasto=="Uber Reñaca"~"Otros",
                                            gasto=="filtro piscina mspa"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Limpieza Alfombra"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Aspiradora"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Limpieza alfombras"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Pila estufa"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Reloj"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Arreglo"~"Electrodomésticos/ Mantención casa",
                                            gasto=="Pan Pepperino"~"Comida",
                                            gasto=="Cookidoo"~"Comida",
                                            gasto=="remedios"~"Farmacia",
                                            gasto=="Bendina Reñaca"~"Gas/Bencina",
                                            gasto=="Bencina Reñaca"~"Gas/Bencina",
                                            gasto=="Vacunas Influenza"~"Farmacia",
                                            gasto=="Remedios"~"Farmacia",
                                            gasto=="Plata fiestas patrias basureros"~"donaciones/regalos",
                                        T~gasto)) %>% 
  dplyr::mutate(fecha_month=factor(fecha_month, levels=format(seq(from = as.Date("2019-03-03"), to = as.Date(substr(Sys.time(),1,10)), by = "1 month"),"%Y-%m")))%>% 
  dplyr::mutate(gasto2=factor(gasto2, levels=c("Agua", "Comida", "Comunicaciones","Electricidad", "Enceres", "Farmacia", "Gas/Bencina", "Diosi", "donaciones/regalos", "Electrodomésticos/ Mantención casa", "VTR", "Netflix", "Otros")))%>% 
    dplyr::group_by(fecha_month, gasto2, .drop=F)%>%
    dplyr::summarise(gasto_total=sum(monto, na.rm = T)/1000)%>%
  data.frame() %>% na.omit()

fit_month_gasto_24<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("2024",fecha_month)) %>% 
    #sacar el ultimo mes
    dplyr::filter(as.character(format(as.Date(substr(Sys.time(),1,10)),"%Y-%m"))!=fecha_month) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame()%>% ungroup()

fit_month_gasto_23<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("2023",fecha_month)) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame()%>% ungroup()

fit_month_gasto_22<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("2022",fecha_month)) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame()%>% ungroup()

fit_month_gasto_21<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("2021",fecha_month)) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame()%>% ungroup()


fit_month_gasto_20<-
fit_month_gasto %>% 
    #dplyr::filter()
    dplyr::filter(grepl("202",fecha_month)) %>% 
    dplyr::group_by(gasto2) %>% 
    dplyr::summarise(gasto_prom=mean(gasto_total, na.rm=T)) %>% 
  data.frame() %>% ungroup()

fit_month_gasto_24 %>% 
dplyr::right_join(fit_month_gasto_23,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_22,by="gasto2") %>%
dplyr::right_join(fit_month_gasto_21,by="gasto2") %>% 
dplyr::right_join(fit_month_gasto_20,by="gasto2") %>% 
  janitor::adorn_totals() %>% 
  #dplyr::select(-3)%>% 
  knitr::kable(format = "markdown", size=12, col.names= c("Item","2024","2023","2022","2021","2020"))
Item 2024 2023 2022 2021 2020
Agua 5.516333 5.195333 5.410333 5.849167 6.4088462
Comida 288.100000 366.009167 310.278417 317.896583 341.6904808
Comunicaciones 0.000000 0.000000 0.000000 0.000000 0.0000000
Electricidad 74.633333 38.104750 47.072333 29.523000 35.5299231
Enceres 38.339333 18.259750 20.086417 14.801167 24.5984615
Farmacia 0.000000 4.733250 1.831667 13.996083 7.9840962
Gas/Bencina 23.665667 35.219333 44.325000 13.583667 27.7886346
Diosi 29.350333 55.804250 31.180667 52.687833 42.4919231
donaciones/regalos 0.000000 0.000000 0.000000 14.340167 5.2830577
Electrodomésticos/ Mantención casa 20.000000 0.000000 3.944000 56.595000 17.1051538
VTR 21.990000 12.829167 25.156667 19.086917 19.2730385
Netflix 5.565667 4.555500 7.151583 7.028750 6.5479615
Otros 0.000000 0.000000 3.151083 0.000000 0.7271731
Total 507.160667 540.710500 499.588167 545.388333 535.4287500
## Joining with `by = join_by(word)`


2. UF Proyectada

Saqué la UF proyectada

#options(max.print=5000)

uf18 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2018.htm")%>% rvest::html_nodes("table")
uf19 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2019.htm")%>% rvest::html_nodes("table")
uf20 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2020.htm")%>% rvest::html_nodes("table")
uf21 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2021.htm")%>% rvest::html_nodes("table")
uf22 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2022.htm")%>% rvest::html_nodes("table")
uf23 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2023.htm")%>% rvest::html_nodes("table")

tryCatch(uf24 <-rvest::read_html("https://www.sii.cl/valores_y_fechas/uf/uf2024.htm")%>% rvest::html_nodes("table"),
    error = function(c) {
      uf24b <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
      
    }
  )

tryCatch(uf24 <-uf24[[length(uf24)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1),
    error = function(c) {
      uf24 <<- cbind.data.frame(Día=NA, variable=NA, value=NA)
    }
)

uf_serie<-
bind_rows(
cbind.data.frame(anio= 2018, uf18[[length(uf18)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2019, uf19[[length(uf19)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2020, uf20[[length(uf20)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2021, uf21[[length(uf21)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2022, uf22[[length(uf22)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),

cbind.data.frame(anio= 2023, uf23[[length(uf23)]] %>% rvest::html_table() %>% data.frame() %>% reshape2::melt(id.vars=1)),
cbind.data.frame(anio= 2024, uf24)
)

uf_serie_corrected<-
uf_serie %>% 
dplyr::mutate(month=plyr::revalue(tolower(.[[3]]),c("ene" = 1, "feb"=2, "mar"=3, "abr"=4, "may"=5, "jun"=6, "jul"=7, "ago"=8, "sep"=9, "oct"=10, "nov"=11, "dic"=12))) %>% 
  dplyr::mutate(value=stringr::str_trim(value), value= sub("\\.","",value),value= as.numeric(sub("\\,",".",value))) %>% 
  dplyr::mutate(date=paste0(sprintf("%02d", .[[2]])," ",sprintf("%02d",as.numeric(month)),", ",.[[1]]), date3=lubridate::parse_date_time(date,c("%d %m, %Y"),exact=T),date2=date3) %>% 
   na.omit()#%>%  dplyr::filter(is.na(date3))
## Warning: There was 1 warning in `dplyr::mutate()`.
## i In argument: `date3 = lubridate::parse_date_time(date, c("%d %m, %Y"), exact
##   = T)`.
## Caused by warning:
## !  47 failed to parse.
#Day of the month as decimal number (1–31), with a leading space for a single-digit number.
#Abbreviated month name in the current locale on this platform. (Also matches full name on input: in some locales there are no abbreviations of names.)

warning(paste0("number of observations:",nrow(uf_serie_corrected),",  min uf: ",min(uf_serie_corrected$value),",  min date: ",min(uf_serie_corrected $date3 )))
## Warning: number of observations:2321, min uf: 26799.01, min date: 2018-01-01
# 
# uf_proyectado <- readxl::read_excel("uf_proyectado.xlsx") %>% dplyr::arrange(Período) %>% 
#   dplyr::mutate(Período= as.Date(lubridate::parse_date_time(Período, c("%Y-%m-%d"),exact=T)))

ts_uf_proy<-
ts(data = uf_serie_corrected$value, 
   start = as.numeric(as.Date("2018-01-01")), 
   end = as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])), frequency = 1,
   deltat = 1, ts.eps = getOption("ts.eps"))

fit_tbats <- forecast::tbats(ts_uf_proy)


fr_fit_tbats<-forecast::forecast(fit_tbats, h=298)

La proyección de la UF a 298 días más 2024-05-09 00:04:58 sería de: 37.727 pesos// Percentil 95% más alto proyectado: 40.830,8

Ahora con un modelo ARIMA automático


arima_optimal_uf = forecast::auto.arima(ts_uf_proy)

  autoplotly::autoplotly(forecast::forecast(arima_optimal_uf, h=298), ts.colour = "darkred",
           predict.colour = "blue", predict.linetype = "dashed")%>% 
  plotly::layout(showlegend = F, 
          yaxis = list(title = "Gastos"),
         xaxis = list(
    title="Fecha",
      ticktext = as.list(seq(from = as.Date("2018-01-01"), 
                                  to = as.Date("2018-01-01")+length(fit_tbats$fitted.values)+298, by = 90)), 
      tickvals = as.list(seq(from = as.numeric(as.Date("2018-01-01")), 
                             to = as.numeric(as.Date("2018-01-01"))+length(fit_tbats$fitted.values)+298, by = 90)),
      tickmode = "array",
    tickangle = 90
    ))
fr_fit_tbats_uf<-forecast::forecast(arima_optimal_uf, h=298)
dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats)),variable) %>% dplyr::summarise(max=max(value)) %>% 
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_uf)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>% 
  dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>% 
  dplyr::arrange(variable) %>% 
  knitr::kable(format="markdown", caption="Tabla. Estimación UF (de aquí a 298 días) según cálculos de gastos mensuales",
               col.names= c("Item","UF Proyectada (TBATS)","UF Proyectada (ARIMA)"))
## No id variables; using all as measure variables
## No id variables; using all as measure variables
Tabla. Estimación UF (de aquí a 298 días) según cálculos de gastos mensuales
Item UF Proyectada (TBATS) UF Proyectada (ARIMA)
Lo.95 37314.39 37312.97
Lo.80 37326.45 37324.65
Point.Forecast 37726.90 38784.71
Hi.80 39482.91 43631.32
Hi.95 40444.95 46196.96


3. Gastos proyectados

Lo haré en base a 2 cálculos: el gasto semanal y el gasto mensual en base a mis gastos desde marzo de 2019. La primera proyección la hice añadiendo el precio del arriendo mensual y partiendo en 2 (porque es con yo y Tami). No se incluye el último mes.

Gastos_casa_nvo <- readr::read_csv(as.character(path_sec),
                               col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador",
                                             "link"),skip=1) %>% 
              dplyr::mutate(fecha= lubridate::parse_date_time(fecha, c("%d/%m/%Y"),exact=T)) %>% 
              dplyr::mutate(fecha_month=strftime(fecha, format = "%Y-%m")) %>%
              dplyr::mutate(day=as.Date(as.character(lubridate::floor_date(fecha, "day"))))
Gastos_casa_m <-
Gastos_casa_nvo %>% dplyr::group_by(fecha_month)%>%
              dplyr::summarise(gasto_total=(sum(monto)+500000)/1000,fecha=first(fecha))%>%
              data.frame()

uf_serie_corrected_m <-
uf_serie_corrected %>% dplyr::mutate(ano_m=paste0(anio,"-",sprintf("%02d",as.numeric(month)))) %>%  dplyr::group_by(ano_m)%>%
              dplyr::summarise(uf=(mean(value))/1000,fecha=first(date3))%>%
              data.frame() %>% 
  dplyr::filter(fecha>="2019-02-28")
#Error: Error in standardise_path(file) : object 'enlace_gastos' not found

ts_uf_serie_corrected_m<-
ts(data = uf_serie_corrected_m$uf[-length(uf_serie_corrected_m$uf)], 
   start = 1, 
   end = nrow(uf_serie_corrected_m), 
   frequency = 1,
   deltat = 1, ts.eps = getOption("ts.eps"))

ts_gastos_casa_m<-
ts(data = Gastos_casa_m$gasto_total[-length(Gastos_casa_m$gasto_total)], 
   start = 1, 
   end = nrow(Gastos_casa_m), 
   frequency = 1,
   deltat = 1, ts.eps = getOption("ts.eps"))

fit_tbats_m <- forecast::tbats(ts_gastos_casa_m)

seq_dates<-format(seq(as.Date("2019/03/01"), by = "month", length = dim(Gastos_casa_m)[1]+12), "%m\n'%y")

autplo2t<-
  autoplotly::autoplotly(forecast::forecast(fit_tbats_m, h=12), ts.colour = "darkred",
           predict.colour = "blue", predict.linetype = "dashed")%>% 
  plotly::layout(showlegend = F, 
          yaxis = list(title = "Gastos (en miles)"),
         xaxis = list(
    title="Fecha",
      ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]), 
      tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
      tickmode = "array"#"array"
    )) 

autplo2t

Ahora asumiendo un modelo ARIMA, e incluimos como regresor al precio de la UF.

paste0("Optimo pero sin regresor")
## [1] "Optimo pero sin regresor"
arima_optimal = forecast::auto.arima(ts_gastos_casa_m)
arima_optimal
## Series: ts_gastos_casa_m 
## ARIMA(1,0,0) with non-zero mean 
## 
## Coefficients:
##          ar1       mean
##       0.2428  1018.1513
## s.e.  0.1280    27.5653
## 
## sigma^2 = 28060:  log likelihood = -404.49
## AIC=814.99   AICc=815.4   BIC=821.37
paste0("Optimo pero con regresor")
## [1] "Optimo pero con regresor"
arima_optimal2 = forecast::auto.arima(ts_gastos_casa_m, xreg=as.numeric(ts_uf_serie_corrected_m[1:(length(Gastos_casa_m$gasto_total))]))
arima_optimal2
## Series: ts_gastos_casa_m 
## Regression with ARIMA(1,0,0) errors 
## 
## Coefficients:
##          ar1  intercept     xreg
##       0.2061   694.2043  10.2795
## s.e.  0.1300   248.5873   7.8333
## 
## sigma^2 = 27809:  log likelihood = -403.68
## AIC=815.37   AICc=816.07   BIC=823.88
forecast_uf<-
cbind.data.frame(fecha=as.Date(seq(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)])),(as.numeric(as.Date(uf_serie_corrected$date3[length(uf_serie_corrected$date3)]))+299),by=1), origin = "1970-01-01"),forecast::forecast(fit_tbats, h=300)) %>% 
  dplyr::mutate(ano_m=stringr::str_extract(fecha,".{7}")) %>% 
  dplyr::group_by(ano_m)%>%
              dplyr::summarise(uf=(mean(`Hi 95`,na.rm=T))/1000,fecha=first(fecha))%>%
            data.frame()
autplo2t2<-
  autoplotly::autoplotly(forecast::forecast(arima_optimal2,xreg=c(forecast_uf$uf[1],forecast_uf$uf), h=12), ts.colour = "darkred",
           predict.colour = "blue", predict.linetype = "dashed")%>% 
  plotly::layout(showlegend = F, 
          yaxis = list(title = "Gastos (en miles)"),
         xaxis = list(
    title="Fecha",
      ticktext = as.list(seq_dates[seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)]), 
      tickvals = as.list(seq(from = 1, to = (dim(Gastos_casa_m)[1]+12), by = 3)),
      tickmode = "array"#"array"
    )) 

autplo2t2
fr_fit_tbats_m<-forecast::forecast(fit_tbats_m, h=12)
fr_fit_tbats_m2<-forecast::forecast(arima_optimal, h=12)
fr_fit_tbats_m3<-forecast::forecast(arima_optimal2, h=12,xreg=c(forecast_uf$uf[1],forecast_uf$uf))

dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m3)),variable) %>% dplyr::summarise(max=max(value)), dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m2)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>% 
dplyr::right_join(dplyr::group_by(reshape2::melt(data.frame(fr_fit_tbats_m)),variable) %>% dplyr::summarise(max=max(value)),by="variable") %>% 
  dplyr::mutate(variable=factor(variable,levels=c("Lo.95","Lo.80","Point.Forecast","Hi.80","Hi.95"))) %>% 
  dplyr::arrange(variable) %>% 
  knitr::kable(format="markdown", caption="Estimación en miles de la plata a gastar en el futuro (de aquí a 12 meses) según cálculos de gastos mensuales",
               col.names= c("Item","Modelo ARIMA con regresor (UF)","Modelo ARIMA sin regresor","Modelo TBATS")) 
## No id variables; using all as measure variables
## No id variables; using all as measure variables
## No id variables; using all as measure variables
Estimación en miles de la plata a gastar en el futuro (de aquí a 12 meses) según cálculos de gastos mensuales
Item Modelo ARIMA con regresor (UF) Modelo ARIMA sin regresor Modelo TBATS
Lo.95 775.9811 679.7095 726.3631
Lo.80 891.5957 796.8561 810.9225
Point.Forecast 1109.9967 1018.1513 998.4389
Hi.80 1328.3978 1239.4465 1278.9932
Hi.95 1444.0124 1356.5931 1458.1362


4. Gastos mensuales (resumen manual)

path_sec2<- paste0("https://docs.google.com/spreadsheets/d/",Sys.getenv("SUPERSECRET"),"/export?format=csv&id=",Sys.getenv("SUPERSECRET"),"&gid=847461368")

Gastos_casa_mensual_2022 <- readr::read_csv(as.character(path_sec2),
                #col_names = c("Tiempo", "gasto", "fecha", "obs", "monto", "gastador","link"),
                skip=0)
## Rows: 80 Columns: 4
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (1): mes_ano
## dbl (3): n, Tami, Andrés
## 
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.
head(Gastos_casa_mensual_2022,5) %>% 
  knitr::kable("markdown",caption="Resumen mensual, primeras 5 observaciones")
Resumen mensual, primeras 5 observaciones
n mes_ano Tami Andrés
1 marzo_2019 175533 68268
2 abril_2019 152640 55031
3 mayo_2019 152985 192219
4 junio_2019 291067 84961
5 julio_2019 241389 205893


(
Gastos_casa_mensual_2022 %>% 
    reshape2::melt(id.var=c("n","mes_ano")) %>%
  dplyr::mutate(gastador=as.factor(variable)) %>% 
  dplyr::select(-variable) %>% 
 ggplot2::ggplot(aes(x = n, y = value, color=gastador)) +
  scale_color_manual(name="Gastador", values=c("red", "blue"))+
  geom_line(size=1) +
  #geom_vline(xintercept = as.Date("2019-06-24"),linetype = "dashed") +
  labs(y="Gastos (en miles)",x="Meses", subtitle="Azul= Tami; Rojo= Andrés") +
  ggtitle( "Gastos Mensuales (total manual)") +
  scale_y_continuous(labels = f <- function(x) paste0(x/1000)) + 
#  scale_color_manual(name = "Gastador", values= c("blue", "red"), labels = c("Tami", "Andrés")) +
#  scale_x_yearweek(breaks = "1 month", minor_breaks = "1 week", labels=date_format("%m/%y")) +
 # guides(color = F)+
  theme_custom() +
  theme(axis.text.x = element_text(vjust = 0.5,angle = 35)) +
  theme(
    panel.border = element_blank(), 
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(), 
    axis.line = element_line(colour = "black")
    )
) %>% ggplotly()


Session Info

Sys.getenv("R_LIBS_USER")
## [1] "D:\\a\\_temp\\Library"
sessionInfo()
## R version 4.1.2 (2021-11-01)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows Server x64 (build 20348)
## 
## Matrix products: default
## 
## locale:
## [1] LC_COLLATE=Spanish_Chile.1252  LC_CTYPE=Spanish_Chile.1252   
## [3] LC_MONETARY=Spanish_Chile.1252 LC_NUMERIC=C                  
## [5] LC_TIME=Spanish_Chile.1252    
## 
## attached base packages:
## [1] grid      stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
##  [1] CausalImpact_1.3.0  bsts_0.9.10         BoomSpikeSlab_1.2.6
##  [4] Boom_0.9.15         scales_1.3.0        ggiraph_0.8.9      
##  [7] tidytext_0.4.1      DT_0.32             janitor_2.2.0      
## [10] autoplotly_0.1.4    rvest_1.0.4         plotly_4.10.4      
## [13] xts_0.13.2          forecast_8.21.1     wordcloud_2.6      
## [16] RColorBrewer_1.1-3  SnowballC_0.7.1     tm_0.7-11          
## [19] NLP_0.2-1           tsibble_1.1.4       lubridate_1.9.3    
## [22] forcats_1.0.0       dplyr_1.1.4         purrr_1.0.2        
## [25] tidyr_1.3.1         tibble_3.2.1        tidyverse_2.0.0    
## [28] gsynth_1.2.1        lattice_0.20-45     GGally_2.2.1       
## [31] ggplot2_3.5.0       gridExtra_2.3       plotrix_3.8-4      
## [34] sparklyr_1.8.4      httr_1.4.7          readxl_1.4.3       
## [37] zoo_1.8-12          stringr_1.5.1       stringi_1.8.3      
## [40] data.table_1.15.0   reshape2_1.4.4      fUnitRoots_4021.80 
## [43] plyr_1.8.9          readr_2.1.5        
## 
## loaded via a namespace (and not attached):
##   [1] uuid_1.2-0          systemfonts_1.0.5   selectr_0.4-2      
##   [4] lazyeval_0.2.2      websocket_1.4.1     crosstalk_1.2.1    
##   [7] listenv_0.9.1       digest_0.6.34       foreach_1.5.2      
##  [10] htmltools_0.5.7     fansi_1.0.6         ggfortify_0.4.16   
##  [13] magrittr_2.0.3      doParallel_1.0.17   tzdb_0.4.0         
##  [16] globals_0.16.2      vroom_1.6.5         sandwich_3.1-0     
##  [19] askpass_1.2.0       timechange_0.3.0    anytime_0.3.9      
##  [22] tseries_0.10-55     colorspace_2.1-0    xfun_0.42          
##  [25] crayon_1.5.2        jsonlite_1.8.8      iterators_1.0.14   
##  [28] glue_1.7.0          gtable_0.3.4        car_3.1-2          
##  [31] quantmod_0.4.26     abind_1.4-5         mvtnorm_1.2-4      
##  [34] DBI_1.2.2           rngtools_1.5.2      Rcpp_1.0.12        
##  [37] lfe_2.9-0           viridisLite_0.4.2   xtable_1.8-4       
##  [40] bit_4.0.5           Formula_1.2-5       htmlwidgets_1.6.4  
##  [43] timeSeries_4032.109 gplots_3.1.3.1      ellipsis_0.3.2     
##  [46] spatial_7.3-14      farver_2.1.1        pkgconfig_2.0.3    
##  [49] nnet_7.3-16         sass_0.4.8          dbplyr_2.4.0       
##  [52] chromote_0.2.0      utf8_1.2.4          labeling_0.4.3     
##  [55] tidyselect_1.2.0    rlang_1.1.3         later_1.3.2        
##  [58] munsell_0.5.0       cellranger_1.1.0    tools_4.1.2        
##  [61] cachem_1.0.8        cli_3.6.2           generics_0.1.3     
##  [64] evaluate_0.23       fastmap_1.1.1       yaml_2.3.8         
##  [67] processx_3.8.3      knitr_1.45          bit64_4.0.5        
##  [70] caTools_1.18.2      future_1.33.1       nlme_3.1-153       
##  [73] doRNG_1.8.6         slam_0.1-50         xml2_1.3.6         
##  [76] tokenizers_0.3.0    compiler_4.1.2      rstudioapi_0.15.0  
##  [79] curl_5.2.0          bslib_0.6.1         highr_0.10         
##  [82] ps_1.7.6            fBasics_4032.96     Matrix_1.6-5       
##  [85] its.analysis_1.6.0  urca_1.3-3          vctrs_0.6.5        
##  [88] pillar_1.9.0        lifecycle_1.0.4     lmtest_0.9-40      
##  [91] jquerylib_0.1.4     bitops_1.0-7        R6_2.5.1           
##  [94] promises_1.2.1      KernSmooth_2.23-20  janeaustenr_1.0.0  
##  [97] parallelly_1.37.0   codetools_0.2-18    ggstats_0.5.1      
## [100] assertthat_0.2.1    boot_1.3-28         gtools_3.9.5       
## [103] MASS_7.3-54         openssl_2.1.1       withr_3.0.0        
## [106] fracdiff_1.5-3      parallel_4.1.2      hms_1.1.3          
## [109] quadprog_1.5-8      timeDate_4032.109   rmarkdown_2.25     
## [112] snakecase_0.11.1    carData_3.0-5       TTR_0.24.4
#save.image("__analisis.RData")

sesion_info <- devtools::session_info()
dplyr::select(
  tibble::as_tibble(sesion_info$packages),
  c(package, loadedversion, source)
) %>% 
  DT::datatable(filter = 'top', colnames = c('Row number' =1,'Variable' = 2, 'Percentage'= 3),
              caption = htmltools::tags$caption(
        style = 'caption-side: top; text-align: left;',
        '', htmltools::em('Packages')),
      options=list(
initComplete = htmlwidgets::JS(
        "function(settings, json) {",
        "$(this.api().tables().body()).css({
            'font-family': 'Helvetica Neue',
            'font-size': '50%', 
            'code-inline-font-size': '15%', 
            'white-space': 'nowrap',
            'line-height': '0.75em',
            'min-height': '0.5em'
            });",#;
        "}")))